Getting Started with Big Data

With the right data, vision, approach, and tools, companies can now gain insights and make connections that have been virtually impossible until recently.

It’s never been easier to get started with big data. Infrastructure and hardware costs have decreased, while capabilities have increased. Accessible analytics platforms make processing large data sets easier than ever before. Solutions are becoming more modular, meaning companies can buy as they go for a phased introduction to analytics.

There are also many new technology players in this space, giving companies many options when it comes to making, buying, or outsourcing analytics technology. To top it off, new talent is closing the skills gap, improving the chances of success with any analytics initiative.

But before you can truly create value with big data analytics, you have to understand what it is.

What Is Big Data?

Big data is the intersection of massive, diverse, and fast-moving data; emerging technologies to store and process such data cheaply; and advanced analytical techniques that extract meaning from the data to solve business problems in new ways.

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Focus on the Three Ts

Gaining momentum with big data requires a focus on the three T’s, which are the hallmarks of companies that are successful with big data initiatives. To maximize the results of any big data initiative, organizations need to:

Build the right team. Create a SWAT team for analytics made up of well-rounded experts in the field. In addition to expertise in analytics, team members should have a deep business perspective so they can decide which solutions will be most effective in the short and long term.

Deploy the right tools. Enable teams to maximize value creation by giving them the right tools for the job. As hardware and software costs have decreased, it’s now possible to outfit each person with a virtual machine and massive amounts of storage for around $15,000 per year. Many industry-standard tools now cost just $5,000 to $15,000 per seat.

Test and learn the most effective approaches. Run two- to three-month experiments that push for rapid results and implementation. This forces you to time-box your analysis. You don't have time to wait around for the perfect infrastructure or solution. Just get started, learn as you go, adapt as required, and scale what works.

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